Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

Mousa, Rimal; Qutaishat, M. A.; Moussa, A.

doi:10.1016/j.eswa.2004.12.028

Cited by 139 publications

(62 citation statements)

References 14 publications

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“…The classification accuracy is defined by Equation (15) The proposed classification algorithm based on NSCT and SVM is tested on all microcalcification images of the MIAS database. Mousa et al (2005) proposed system based on wavelet analysis and fuzzyneural, the maximum classification rate obtained was 87.5%. Zyod and Abdel-Qader (2011) proposed a system using GLCM features with PSO-KNN feature selection method.…”

Section: Resultsmentioning

confidence: 90%

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A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

Jasmine¹

2013

American Journal of Engineering and Applied Sciences

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Mammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by extracting the microcalcification features from the Contourlet coefficients of the image and the outcomes are used as an input to the SVM for classification. The system classifies the mammogram images as normal or abnormal and the abnormal severity as benign or malignant. The evaluation of the system is carried on using Mammography Image Analysis Society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate.

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Section: Resultsmentioning

confidence: 90%

“…It can be concluded that the maximum successful classification rate using wavelet (Mousa et al, 2005) was 87.5% obtained by the features extracted at the decomposition level 2-3. For NSCT, the maximum successful classification accuracy rate obtained is 96.15%.…”

Section: Ajeasmentioning

confidence: 99%

A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

Jasmine¹

2013

American Journal of Engineering and Applied Sciences

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“…(3) Signal processing methods: In this class, texture features are obtained according to either pixel characteristics or image frequency spectrum including Laws energy filtering [6], Gabor filtering [6], and wavelet [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Various methods have been proposed with the most efficient ones known as Bayesian classifier [13], multilayer perceptron [9,14], adaptive neuron fuzzy inference system (ANFIS) classifier [12], radial basis function (RBF) [14], k-nearest neighbors (KNN) [8], decision tree classifier [15], and support vector machines (SVM) [16].…”

Section: Introductionmentioning

confidence: 99%

Multiresolution and Multiscale Geometric Analysis based Breast Cancer Diagnosis using weighted SVM

Wang¹,

Yin²

2016

Proceedings of the 2015 International Conference on Mechanical Science and Engineering

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Abstract. This paper presents an approach for breast cancer diagnosis in digital mammogram using multiresolution and multiscale geometric analysis. The proposed method consists of two stages. In the first stage, mammogram images are decomposed into different resolution levels using wavelet transform and curvelet transform, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted as features vector. In the second stage, classification is performed on a weighted support vector machine (SVM). Due to random selection of samples, it is highly probable that a significantly small portion of the training set is the "mass present" class. To address this problem, we propose to use weighted SVM in a successive enhancement learning scheme to examine all the available "mass present" samples. The proposed approach is applied to the Mammograms Image Analysis Society dataset (MIAS) and classification accuracy of 99.3% is determined over an efficient computation time by successive learning enhancement. Experiment results illustrate that the multiresolution and multiscale geometric analysis-based feature extraction in conjunction with the state-of-art classifier construct a powerful, efficient and practical approach for breast cancer diagnosis.

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“…Mammography is the major screening tool which is carried out for detection of breast cancer at early stage and by the use of mammography at least 30% drop in breast cancer losses [5]. But some of the breast lesions such as micro-calcification, breast masses, shape distortion, and irregularity between breasts may not be detected by screening mammography because it is very difficult to interpret the morphological features [6]. Dense breast parenchyma is highly challenging job for sustaining sensitivity of mammography which depreciates both recognition and classification tasks [7].…”

Section: Introductionmentioning

confidence: 99%

Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features

Singh¹,

Sharma²

2018

Preprint

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Breast cancer is the second cause of fatality among all cancers for women. Automatic classification of breast cancer lesions in mammograms is a challenging task due to the irregularity and complexity of the location, size, shape, and texture of these lesions. The intensity dissimilarity has been found between breast cancer tissues and normal tissues, when a multispectral anatomical mammographic screening scans have been done. In this work, two approaches have been evaluated to classify the breast tumor lesions. The first one is through Gabor wavelet features and the second one is Statistical features. Subsequently, support vector machine, Multilayer Perceptron and KNN classifiers have been used with computer based method for breast tumor classification.

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Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

Cited by 139 publications

References 14 publications

A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

Multiresolution and Multiscale Geometric Analysis based Breast Cancer Diagnosis using weighted SVM

Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features

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